Overview

Dataset statistics

Number of variables13
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.6 KiB
Average record size in memory98.3 B

Variable types

NUM12
BOOL1

Warnings

account has unique values Unique

Reproduction

Analysis started2021-05-15 07:59:46.428235
Analysis finished2021-05-15 08:00:09.295557
Duration22.87 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

account
Real number (ℝ≥0)

UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2068103284
Minimum37709441
Maximum4281711154
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-15T18:00:09.388578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum37709441
5-th percentile151250444.9
Q11212680590
median1994605610
Q32974652188
95-th percentile4059914832
Maximum4281711154
Range4244001713
Interquartile range (IQR)1761971598

Descriptive statistics

Standard deviation1209468316
Coefficient of variation (CV)0.5848200742
Kurtosis-0.987534946
Mean2068103284
Median Absolute Deviation (MAD)931986363
Skewness0.1087223221
Sum2.068103284e+11
Variance1.462813608e+18
MonotocityStrictly increasing
2021-05-15T18:00:09.541351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
267306905511.0%
 
21179248911.0%
 
134983457311.0%
 
374188091311.0%
 
425850272311.0%
 
387925870911.0%
 
427427285411.0%
 
290167228211.0%
 
406565257511.0%
 
3892387411.0%
 
Other values (90)9090.0%
 
ValueCountFrequency (%) 
3770944111.0%
 
3892387411.0%
 
5350854611.0%
 
8038849411.0%
 
9081474911.0%
 
ValueCountFrequency (%) 
428171115411.0%
 
427427285411.0%
 
425850272311.0%
 
416382218611.0%
 
406565257511.0%
 

age
Real number (ℝ≥0)

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.77
Minimum18
Maximum78
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-15T18:00:09.687385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile19
Q122
median29.5
Q339.25
95-th percentile50.1
Maximum78
Range60
Interquartile range (IQR)17.25

Descriptive statistics

Standard deviation11.54425389
Coefficient of variation (CV)0.3633696536
Kurtosis2.331432564
Mean31.77
Median Absolute Deviation (MAD)8.5
Skewness1.242431212
Sum3177
Variance133.269798
MonotocityNot monotonic
2021-05-15T18:00:09.819401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%) 
1966.0%
 
2066.0%
 
2166.0%
 
3866.0%
 
3555.0%
 
4055.0%
 
2255.0%
 
2655.0%
 
2444.0%
 
2544.0%
 
Other values (23)4848.0%
 
ValueCountFrequency (%) 
1844.0%
 
1966.0%
 
2066.0%
 
2166.0%
 
2255.0%
 
ValueCountFrequency (%) 
7811.0%
 
6911.0%
 
6411.0%
 
5311.0%
 
5211.0%
 

X
Real number (ℝ)

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-38.6475
Minimum-573
Maximum-12.37
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-15T18:00:09.976436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-573
5-th percentile-37.931
Q1-37.76
median-33.975
Q3-31.895
95-th percentile-22.7555
Maximum-12.37
Range560.63
Interquartile range (IQR)5.865

Descriptive statistics

Standard deviation54.27292856
Coefficient of variation (CV)-1.404306321
Kurtosis97.75734874
Mean-38.6475
Median Absolute Deviation (MAD)3.7
Skewness-9.830716027
Sum-3864.75
Variance2945.550774
MonotocityNot monotonic
2021-05-15T18:00:10.120469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-37.8444.0%
 
-37.7633.0%
 
-37.6622.0%
 
-37.6922.0%
 
-33.7622.0%
 
-37.9122.0%
 
-37.8222.0%
 
-33.822.0%
 
-31.922.0%
 
-33.7722.0%
 
Other values (75)7777.0%
 
ValueCountFrequency (%) 
-57311.0%
 
-42.8811.0%
 
-38.0311.0%
 
-37.9711.0%
 
-37.9511.0%
 
ValueCountFrequency (%) 
-12.3711.0%
 
-12.4511.0%
 
-12.4911.0%
 
-17.0311.0%
 
-21.1511.0%
 

Y
Real number (ℝ≥0)

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143.5658
Minimum114.62
Maximum255
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-15T18:00:10.272514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum114.62
5-th percentile115.79
Q1143.565
median145.155
Q3150.905
95-th percentile153.0905
Maximum255
Range140.38
Interquartile range (IQR)7.34

Descriptive statistics

Standard deviation16.19440982
Coefficient of variation (CV)0.1128013066
Kurtosis22.25234251
Mean143.5658
Median Absolute Deviation (MAD)5.745
Skewness2.682219338
Sum14356.58
Variance262.2589095
MonotocityNot monotonic
2021-05-15T18:00:10.417580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
144.9633.0%
 
151.2733.0%
 
150.922.0%
 
149.0322.0%
 
145.0322.0%
 
151.0422.0%
 
145.0422.0%
 
115.7922.0%
 
144.8922.0%
 
151.2322.0%
 
Other values (77)7878.0%
 
ValueCountFrequency (%) 
114.6211.0%
 
115.7211.0%
 
115.7411.0%
 
115.7811.0%
 
115.7922.0%
 
ValueCountFrequency (%) 
25511.0%
 
153.4122.0%
 
153.3211.0%
 
153.111.0%
 
153.0911.0%
 

gender_M
Boolean

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size100.0 B
1
56 
0
44 
ValueCountFrequency (%) 
15656.0%
 
04444.0%
 
2021-05-15T18:00:10.920684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

balance
Real number (ℝ≥0)

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137686.6746
Minimum13769.63
Maximum1584768.28
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-15T18:00:11.024718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum13769.63
5-th percentile22724.108
Q149657.2425
median72277.425
Q3114188.5225
95-th percentile412686.295
Maximum1584768.28
Range1570998.65
Interquartile range (IQR)64531.28

Descriptive statistics

Standard deviation229337.4936
Coefficient of variation (CV)1.665647705
Kurtosis24.65345916
Mean137686.6746
Median Absolute Deviation (MAD)31959.62
Skewness4.640966909
Sum13768667.46
Variance5.259568595e+10
MonotocityNot monotonic
2021-05-15T18:00:11.182743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
83700.4222.0%
 
27141.6711.0%
 
381472.2411.0%
 
95447.7711.0%
 
51624.9511.0%
 
399484.9711.0%
 
76688.0511.0%
 
36491.3811.0%
 
116438.4311.0%
 
93731.4311.0%
 
Other values (89)8989.0%
 
ValueCountFrequency (%) 
13769.6311.0%
 
14129.5811.0%
 
16864.5911.0%
 
17476.4411.0%
 
20776.7611.0%
 
ValueCountFrequency (%) 
1584768.2811.0%
 
1398902.5511.0%
 
792776.2911.0%
 
506145.7211.0%
 
467645.2211.0%
 

amount
Real number (ℝ≥0)

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17002.6969
Minimum7155.96
Maximum35343.92
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-15T18:00:11.356782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum7155.96
5-th percentile9199.968
Q111684.5975
median14887.63
Q321597.75
95-th percentile30883.351
Maximum35343.92
Range28187.96
Interquartile range (IQR)9913.1525

Descriptive statistics

Standard deviation6894.280033
Coefficient of variation (CV)0.4054815582
Kurtosis-0.09319912142
Mean17002.6969
Median Absolute Deviation (MAD)3833.95
Skewness0.8870960545
Sum1700269.69
Variance47531097.17
MonotocityNot monotonic
2021-05-15T18:00:11.498815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
33081.8822.0%
 
19170.0611.0%
 
8703.8411.0%
 
13312.3811.0%
 
12214.4611.0%
 
16542.1111.0%
 
11573.8211.0%
 
18010.3711.0%
 
13767.0411.0%
 
18531.6611.0%
 
Other values (89)8989.0%
 
ValueCountFrequency (%) 
7155.9611.0%
 
748811.0%
 
8603.8811.0%
 
8637.5911.0%
 
8703.8411.0%
 
ValueCountFrequency (%) 
35343.9211.0%
 
33644.1311.0%
 
33081.8822.0%
 
31762.1211.0%
 
30837.111.0%
 

status_posted
Real number (ℝ≥0)

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.77
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-15T18:00:11.629855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.95
Q16
median7
Q313
95-th percentile14
Maximum14
Range12
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.648314956
Coefficient of variation (CV)0.4159994249
Kurtosis-1.411658234
Mean8.77
Median Absolute Deviation (MAD)1
Skewness0.1845438928
Sum877
Variance13.31020202
MonotocityNot monotonic
2021-05-15T18:00:11.747915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
62929.0%
 
132727.0%
 
72424.0%
 
1488.0%
 
244.0%
 
1244.0%
 
422.0%
 
311.0%
 
511.0%
 
ValueCountFrequency (%) 
244.0%
 
311.0%
 
422.0%
 
511.0%
 
62929.0%
 
ValueCountFrequency (%) 
1488.0%
 
132727.0%
 
1244.0%
 
72424.0%
 
62929.0%
 

sales_pos
Real number (ℝ≥0)

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1576.5066
Minimum19.24
Maximum5768.52
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-15T18:00:11.889937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum19.24
5-th percentile217.3015
Q1723.345
median1359.54
Q32069.325
95-th percentile3519.651
Maximum5768.52
Range5749.28
Interquartile range (IQR)1345.98

Descriptive statistics

Standard deviation1163.271588
Coefficient of variation (CV)0.7378793009
Kurtosis1.752857806
Mean1576.5066
Median Absolute Deviation (MAD)682.125
Skewness1.233192866
Sum157650.66
Variance1353200.787
MonotocityNot monotonic
2021-05-15T18:00:12.037971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
645.5522.0%
 
5768.5211.0%
 
2634.9611.0%
 
2124.9411.0%
 
1229.8611.0%
 
1534.6711.0%
 
410.0411.0%
 
1296.6611.0%
 
2835.8211.0%
 
1940.8811.0%
 
Other values (89)8989.0%
 
ValueCountFrequency (%) 
19.2411.0%
 
91.8511.0%
 
104.5511.0%
 
170.7211.0%
 
192.6311.0%
 
ValueCountFrequency (%) 
5768.5211.0%
 
5020.7111.0%
 
4988.2411.0%
 
4556.7511.0%
 
3622.2711.0%
 

pos
Real number (ℝ≥0)

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1564.5785
Minimum19.76
Maximum8244.07
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-15T18:00:12.210010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum19.76
5-th percentile220.0785
Q1672.4175
median1228.48
Q31884.45
95-th percentile3671.602
Maximum8244.07
Range8224.31
Interquartile range (IQR)1212.0325

Descriptive statistics

Standard deviation1335.591392
Coefficient of variation (CV)0.8536429406
Kurtosis7.702648457
Mean1564.5785
Median Absolute Deviation (MAD)592.535
Skewness2.340894403
Sum156457.85
Variance1783804.365
MonotocityNot monotonic
2021-05-15T18:00:12.354043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3667.8222.0%
 
1538.7211.0%
 
2251.2711.0%
 
1845.1611.0%
 
969.8311.0%
 
786.9111.0%
 
1369.511.0%
 
1700.8811.0%
 
343.8411.0%
 
1306.7211.0%
 
Other values (89)8989.0%
 
ValueCountFrequency (%) 
19.7611.0%
 
31.2611.0%
 
122.8611.0%
 
160.6811.0%
 
209.611.0%
 
ValueCountFrequency (%) 
8244.0711.0%
 
6636.1311.0%
 
5615.7911.0%
 
4830.9711.0%
 
3743.4611.0%
 

payment
Real number (ℝ≥0)

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.45
Minimum338
Maximum5332
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-15T18:00:12.501086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum338
5-th percentile503.1
Q11209.25
median1808
Q32611
95-th percentile4138.4
Maximum5332
Range4994
Interquartile range (IQR)1401.75

Descriptive statistics

Standard deviation1091.421207
Coefficient of variation (CV)0.5420652147
Kurtosis0.3310755267
Mean2013.45
Median Absolute Deviation (MAD)682
Skewness0.8319652202
Sum201345
Variance1191200.25
MonotocityNot monotonic
2021-05-15T18:00:12.648119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
48633.0%
 
173422.0%
 
166022.0%
 
244211.0%
 
191711.0%
 
184611.0%
 
172911.0%
 
99511.0%
 
88411.0%
 
187811.0%
 
Other values (86)8686.0%
 
ValueCountFrequency (%) 
33811.0%
 
39011.0%
 
48633.0%
 
50411.0%
 
57911.0%
 
ValueCountFrequency (%) 
533211.0%
 
467111.0%
 
458111.0%
 
447211.0%
 
429811.0%
 

inter_bank
Real number (ℝ≥0)

Distinct66
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean867.95
Minimum213
Maximum3794
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-15T18:00:12.804146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum213
5-th percentile263.8
Q1570.75
median702
Q3903.75
95-th percentile1965.6
Maximum3794
Range3581
Interquartile range (IQR)333

Descriptive statistics

Standard deviation613.7754562
Coefficient of variation (CV)0.7071553156
Kurtosis9.134588169
Mean867.95
Median Absolute Deviation (MAD)164
Skewness2.699970767
Sum86795
Variance376720.3106
MonotocityNot monotonic
2021-05-15T18:00:12.949177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7023434.0%
 
77122.0%
 
157911.0%
 
42911.0%
 
53611.0%
 
83711.0%
 
21311.0%
 
116411.0%
 
23411.0%
 
47711.0%
 
Other values (56)5656.0%
 
ValueCountFrequency (%) 
21311.0%
 
22811.0%
 
23411.0%
 
25011.0%
 
26011.0%
 
ValueCountFrequency (%) 
379411.0%
 
367311.0%
 
283311.0%
 
216011.0%
 
214811.0%
 

phone_bank
Real number (ℝ≥0)

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1475.52
Minimum246
Maximum1916
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-15T18:00:13.093221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum246
5-th percentile472.8
Q11629
median1629
Q31629
95-th percentile1629
Maximum1916
Range1670
Interquartile range (IQR)0

Descriptive statistics

Standard deviation397.1359963
Coefficient of variation (CV)0.2691498566
Kurtosis3.12936158
Mean1475.52
Median Absolute Deviation (MAD)0
Skewness-2.192116927
Sum147552
Variance157716.9996
MonotocityNot monotonic
2021-05-15T18:00:13.212248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
16298585.0%
 
50411.0%
 
49211.0%
 
47411.0%
 
54611.0%
 
24611.0%
 
87111.0%
 
40211.0%
 
79311.0%
 
35511.0%
 
Other values (6)66.0%
 
ValueCountFrequency (%) 
24611.0%
 
25211.0%
 
35511.0%
 
40211.0%
 
45011.0%
 
ValueCountFrequency (%) 
191611.0%
 
16298585.0%
 
87111.0%
 
79311.0%
 
69611.0%
 

Interactions

2021-05-15T17:59:49.541958image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:49.662987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:49.788015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:49.911053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:50.039082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:50.172112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:50.290139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:50.411166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:50.540185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:50.660212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:50.780240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:50.901268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:51.024296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:51.146324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:51.273352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:51.402381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:51.534411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:51.683445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:51.823476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:51.954506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:52.087536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:52.207577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-15T17:59:58.784933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:58.909961image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:59.035990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:59.155006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:59.283036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:59.411100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-15T17:59:59.658155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:59.788185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T17:59:59.924218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T18:00:00.058398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T18:00:00.199420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-15T18:00:00.586519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T18:00:00.709546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T18:00:00.831577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-15T18:00:08.475360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T18:00:08.612392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-05-15T18:00:13.350290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-15T18:00:13.602336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-15T18:00:13.850393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-15T18:00:14.105461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-05-15T18:00:08.862448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-15T18:00:09.175540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

accountageXYgender_Mbalanceamountstatus_postedsales_pospospaymentinter_bankphone_bank
03770944118-28.01153.41198107.0328925.617.0399.84663.052088.01692.0252.0
13892387438-33.90151.271506145.7224452.3513.0734.411716.744671.0827.01629.0
25350854635-33.76150.62054704.118703.8412.01204.22350.311180.0702.01629.0
38038849428-37.42144.97135050.3211499.066.03033.071356.47852.0270.01629.0
49081474935-32.98151.68165301.3313852.026.01472.621845.16831.0902.01916.0
515443127118-27.48153.09017476.449226.086.01229.862390.651734.0883.01629.0
618244657427-32.00116.06050023.1019881.057.03048.653483.64945.01716.01629.0
721179248930-34.93138.63014129.589821.802.01763.542014.291801.0702.01629.0
824080474329-30.75121.481106299.1030837.107.01876.13766.902747.0702.0559.0
935410665839-33.80151.04078807.9425517.555.02567.761597.611635.0350.0531.0

Last rows

accountageXYgender_Mbalanceamountstatus_postedsales_pospospaymentinter_bankphone_bank
90387925870940-37.66143.8301398902.5529512.287.01527.511422.822672.0917.0492.0
91388103119037-21.15149.19172025.4311001.0012.0410.041426.07996.0702.01629.0
92394118108725-31.94115.79154667.7116127.106.0297.19160.682000.0576.01629.0
93395467788747-32.28115.72075920.3216542.1113.0871.691023.704094.0702.01629.0
94405961284538-12.49130.981399484.9718531.667.0242.31409.651734.0585.01629.0
95406565257521-31.82115.81183700.4225050.557.03369.45606.373957.01001.01629.0
96416382218626-34.97149.03094134.8711048.5212.0104.5519.762442.0702.01629.0
97425850272324-37.74145.45051624.9513777.9213.02662.223667.821984.0670.01629.0
98427427285420-37.86145.23142000.309844.986.0233.121369.501183.0702.01629.0
99428171115442-37.84144.981275038.6612304.747.02054.40122.861438.0702.0504.0